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We propose a novel framework for training neural networks which is capable oflearning 3D information of non-rigid objects when only 2D annotations areavailable as ground truths. Recently, there have been some approaches thatincorporate the problem setting of non-rigid structure-from-motion (NRSfM) intodeep learning to learn 3D structure reconstruction. The most importantdifficulty of NRSfM is to estimate both the rotation and deformation at thesame time, and previous works handle this by regressing both of them. In thispaper, we resolve this difficulty by proposing a loss function wherein thesuitable rotation is automatically determined. Trained with the cost functionconsisting of the reprojection error and the low-rank term of aligned shapes,the network learns the 3D structures of such objects as human skeletons andfaces during the training, whereas the testing is done in a single-frame basis.The proposed method can handle inputs with missing entries and experimentalresults validate that the proposed framework shows superior reconstructionperformance to the state-of-the-art method on the Human 3.6M, 300-VW, andSURREAL datasets, even though the underlying network structure is very simple.

Procrustean Regression Networks: Learning 3D Structure of Non-Rigid Objects from 2D Annotations